Automatic detection of arrhythmias is important for diagnosis of heart problems. Wavelet analysis is suitable to extract information from non-stationary signals like the ECG because of its ability to decompose the signal at various resolutions. In this paper we present an improved version of novel method to separate abnormal beats and normal beats within the ECG signals, using wavelet analysis and feature extraction. Two feature extraction methods were analysed, one based on the normalised energy and one based on the entropy of the beat to beat signal. Results indicate that eliminating normal beats which occur before and after the abnormal beats, improved the separation between normal and abnormal.